The development of modern imaging techniques in addition to the implementing hardware to carry them out have provided the medically schooled eye of the medical practitioner with new diagnostic possibilities for the medical practitioner.
In medical technology, the imaging-implementing hardware is also referred to as a modality and it also comprises a large number of tomographs, such as CT, PET or MRI, in addition to the classical X-ray equipment. These modalities acquire a large number of highly-resolved slice images of the patient, all of which are subsequently available as three-dimensional (3D) image data blocks for further examination. The 3D image data blocks require a lot of storage space and are therefore maintained centrally for diagnostic purposes in database systems such as PACS.
Using modern rendering methods such as VRT (volume rendering technique), MIP (maximum intensity projection) or SSD (surface shaded display), a number of two-dimensional images having visual (e.g. color) embodiments which assist the diagnosis are generated from the 3D image data blocks by way of a projection in a desired direction and are then made available to the medical practitioner for diagnostic purposes.
By way of example, these two-dimensional (2D) images are then examined by the medical practitioner in an advantageous arrangement or sequence on the basis of a hanging protocol, or the images are displayed in a continuously refreshed state by means of an animation or user interaction. For example, an operation by which the medical practitioner can examine an organ from different perspectives is feasible. Each perspective is illustrated by one image of the organ and a new perspective of the organ is loaded as a new image by a mouse click, for example. The quality of the 2D images, that is to say the resolution and the color fidelity are of the utmost importance for a reliable diagnosis.
The modalities are generally integrated as central nodes into a medical communication network, such as a hospital intranet or supra-regional or supranational networks (internet), and from these nodes the images can be transmitted to the enquiring nodes (clients).
Integrating the modalities as central nodes is necessary because the modalities are very expensive infrastructures. For example, not every practice or even every hospital can afford its own CT.
Implementing the rendering method to generate the 2D images from the 3D image bock is also very intensive computationally and for this reason it is also in part implemented centrally by specialized high-performance computers.
Centralizing the modalities or the central provision of the three-dimensional image data, for example by a PACS, in the network then results in the following practical application scenario for the medical practitioner: by means of the client and the network, the medical practitioner will request a specific rendering method for the images from the 3D image data block which suits the particular diagnostic procedure. The previously acquired 3D image data block of the patient to be examined is then made available via PACS and is rendered by the central graphics computer. The multiplicity of 2D images generated in this fashion are then transmitted to the enquiring client.
However, since the medical practitioner also wishes to use the rendered 2D images for emergency diagnoses for example, and/or because these images have to be made available to medical practitioners at a multiplicity of widely distributed nodes in the communication network, for example within the scope of a medical teleconference, the fast transmission of the qualitatively high-grade 2D images to the clients or nodes is of enormous importance.
Although the rendered 2D images are slices of the memory-intensive 3D image data block, they generally still are memory intensive, so compression methods have to be applied for a high throughput rate when transmitted over the network. However, the above-described functionality of the user interaction to display different perspectives, for example, which requires a multiplicity of 2D images to be transmitted as frames in possibly short time intervals, may also be memory-intensive as a result of high data traffic.
However, this results in the problem of artifacts being generated in these 2D images when, in particular, lossy compression methods are used, in particular if the 2D images also have a proportion of 3D graphics elements such as lines, text, meshes, etc. However, these artifacts can be extremely detrimental to the quality of the images and can, as a worst case scenario, lead to false diagnoses.
If these graphics components in the 2D images are only overlay graphics, the problem can be circumvented for example by compressing and transmitting the graphics components separately from the 2D images and only combining them at the target node.
However, if they are not overlay graphics, i.e. if the graphics elements are directly embedded in the 2D image or covered, then this is not possible, and so the application of an arbitrary lossy compression method inevitably results in the mentioned artifacts.
The prior art discloses a number of storage and compression methods, such as DjVu, which are able to isolate the graphics elements by complex pattern and image recognition routines so that the image to be compressed is decomposed into foreground and background components, and these can then be compressed separately. Foreground and background components of the image are then compressed separately and recombined at the target node after transmission and decompression.
The disadvantage of intelligent compression methods such as DjVu is that they are quite complex algorithms, the implementation of which is computationally intensive and requires a lot of time. Quick transmission of images, which is often required in a medical scenario, is not possible using such compression methods if artifact formation is to be circumvented.
Thus, in principle there are three options for avoiding the “artifact problem” when transmitting images in a server-client scenario: there is no lossy transmission of the images, i.e. a very low compression rate is utilized; however, a problem associated with this is that only very low throughput rates can be achieved. Another possibility is to do without images with 3D graphics components as a matter of principle; however, this is paid for by the disadvantage that the visualization option required for diagnosis cannot be fully utilized, which is unacceptable, particularly in the medical field. A third possibility of course lies in accepting artifact formation, but in certain circumstances this can lead to false diagnoses when interpreting the images.